About this trial
Colorectal cancer (CRC) ranks third in both incidence and mortality among all malignant tumors in China. Studies have shown that early screening can significantly reduce its incidence and mortality. Colonoscopy is the gold standard for CRC screening; however, compliance with colonoscopy among high-risk groups in China is very low. Artificial intelligence (AI)-assisted tools can provide real-time, personalized health education, and nudge strategies can help translate intent into action. This trial aims to evaluate the effectiveness of AI-empowered nudge for improving colonoscopy uptake among high-risk individuals aged 45 to 74 in China. It's a two-arm, pragmatic cluster randomized controlled trial. The main question it aims to answer is whether the AI-enabled personalized health education and nudge strategies improve colonoscopy adherence.
Participants will:
1. Be recruited and allocated into one of two groups according to the assigned clusters. Participants in one group will be invited to receive usual care. In addition to usual care, participants in the other group will receive AI-empowered nudge, featuring an AI chatbot providing real-time personalized responses and a nudge environment with default screening option. 2. Have their colonoscopy status checked at the end of trial.
Eligibility criteria
Qualifiers
Aged 45-74 years;
Test positive on the Colorectal Cancer Risk Assessment Scale and the immunochemical fecal occult blood test;
In good general health, mentally competent;
Provide informed consent.
Disqualifiers
History of colorectal resection;
Previous diagnosis of cancer or currently undergoing any cancer-related treatment;
Underwent a colonoscopy or sigmoidoscopy within the past 5 years;
Contraindications to colonoscopy (e.g. severe cardiac, cerebral, lung diseases, or renal dysfunction).
Trial design
Treatments tested in this trial
- AI-empowered nudge (AINC) strategy
- Usual Care
Treatment groups
Locations
Sponsors and collaborators
Fudan University
Lead sponsor
Nantong Center for Disease Control and Prevention
Collaborator